What is a Machine Learning F1 Score?

An F1 score is a metric used in machine learning (ML) to evaluate how accurately a?binary classification model?classifies new input, taking both?precision?and?recall?metrics into account. F1 scores combine these two metrics to create a single score that represents the overall accuracy of the model.

• Precision?measures how often the model is correct when it predicts a positive instance.
• Recall?measures how well the model is able to find all the positive instances in a dataset.

F1 scores are often used to compare the performance of different models or to optimize the?hyperparameters?of a single model. This metric is especially useful when one class in the data set that's used to train the model has significantly more instances than the other class.

F1 scores are often used to evaluate binary classification models designed to:

• Classify emails as either being spam or not spam.
• Classify medical images as being either positive or negative for a specific condition.
• Classify loan applications as either being high or low risk.
• Classify financial transactions as being either fraudulent or non-fraudulent.
• Classify text data as having either a positive or negative sentiment.

How F1 Scores Are Calculated

Mathematically, F1 scores represent the?harmonic mean?of precision and recall. Harmonic mean is a type of average that's used when the values that are being averaged are ratios. It is calculated by taking the reciprocal of each value, finding their average and then using the reciprocal of that average as the mean.

Using the harmonic mean puts more weight on smaller values. If one of the values that is being averaged is extremely low, the impact of the low value will be appropriately reflected in the F1 score.

Scores range from 0 to 1. The higher score, the more accurate the outputs.